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lax_test.py
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# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
from functools import partial
import itertools
from unittest import SkipTest
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
import jax
from jax import api
from jax import core
from jax import dtypes
from jax import lax
from jax import test_util as jtu
from jax import tree_util
from jax import lax_reference
from jax.test_util import check_grads
import jax.util
from jax.util import prod
from jax import xla
from jax._src.lax.lax import _device_put_raw
from jax.config import config
config.parse_flags_with_absl()
FLAGS = config.FLAGS
### lax tests
# For standard unops and binops, we can generate a large number of tests on
# arguments of appropriate shapes and dtypes using the following table.
float_dtypes = jtu.dtypes.all_floating
complex_elem_dtypes = jtu.dtypes.floating
complex_dtypes = jtu.dtypes.complex
inexact_dtypes = jtu.dtypes.all_inexact
int_dtypes = jtu.dtypes.all_integer
uint_dtypes = jtu.dtypes.all_unsigned
bool_dtypes = jtu.dtypes.boolean
default_dtypes = float_dtypes + int_dtypes
all_dtypes = float_dtypes + complex_dtypes + int_dtypes + bool_dtypes
compatible_shapes = [[(3,)], [(3, 4), (3, 1), (1, 4)], [(2, 3, 4), (2, 1, 4)]]
OpRecord = collections.namedtuple(
"OpRecord", ["op", "nargs", "dtypes", "rng_factory", "tol"])
def op_record(op, nargs, dtypes, rng_factory, tol=None):
return OpRecord(op, nargs, dtypes, rng_factory, tol)
LAX_OPS = [
op_record("neg", 1, default_dtypes + complex_dtypes, jtu.rand_small),
op_record("sign", 1, default_dtypes + uint_dtypes, jtu.rand_small),
op_record("floor", 1, float_dtypes, jtu.rand_small),
op_record("ceil", 1, float_dtypes, jtu.rand_small),
op_record("round", 1, float_dtypes, jtu.rand_default),
op_record("nextafter", 2, [f for f in float_dtypes if f != dtypes.bfloat16],
jtu.rand_default, tol=0),
op_record("is_finite", 1, float_dtypes, jtu.rand_small),
op_record("exp", 1, float_dtypes + complex_dtypes, jtu.rand_small),
# TODO(b/142975473): on CPU, expm1 for float64 is only accurate to ~float32
# precision.
op_record("expm1", 1, float_dtypes + complex_dtypes, jtu.rand_small,
{np.float64: 1e-8}),
op_record("log", 1, float_dtypes + complex_dtypes, jtu.rand_positive),
op_record("log1p", 1, float_dtypes + complex_dtypes, jtu.rand_positive),
# TODO(b/142975473): on CPU, tanh for complex128 is only accurate to
# ~float32 precision.
# TODO(b/143135720): on GPU, tanh has only ~float32 precision.
op_record("tanh", 1, float_dtypes + complex_dtypes, jtu.rand_small,
{np.float64: 1e-9, np.complex128: 1e-7}),
op_record("sin", 1, float_dtypes + complex_dtypes, jtu.rand_default),
op_record("cos", 1, float_dtypes + complex_dtypes, jtu.rand_default),
op_record("atan2", 2, float_dtypes, jtu.rand_default),
op_record("sqrt", 1, float_dtypes + complex_dtypes, jtu.rand_positive),
op_record("rsqrt", 1, float_dtypes + complex_dtypes, jtu.rand_positive),
op_record("square", 1, float_dtypes + complex_dtypes, jtu.rand_default),
op_record("reciprocal", 1, float_dtypes + complex_dtypes, jtu.rand_positive),
op_record("tan", 1, float_dtypes + complex_dtypes, jtu.rand_default, {np.float32: 3e-5}),
op_record("asin", 1, float_dtypes + complex_dtypes, jtu.rand_small),
op_record("acos", 1, float_dtypes + complex_dtypes, jtu.rand_small),
op_record("atan", 1, float_dtypes + complex_dtypes, jtu.rand_small),
op_record("asinh", 1, float_dtypes + complex_dtypes, jtu.rand_default,
tol={np.complex64: 1E-4, np.complex128: 1E-5}),
op_record("acosh", 1, float_dtypes + complex_dtypes, jtu.rand_positive),
# TODO(b/155331781): atanh has only ~float precision
op_record("atanh", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-9}),
op_record("sinh", 1, float_dtypes + complex_dtypes, jtu.rand_default),
op_record("cosh", 1, float_dtypes + complex_dtypes, jtu.rand_default),
op_record("lgamma", 1, float_dtypes, jtu.rand_positive,
{np.float32: 1e-3 if jtu.device_under_test() == "tpu" else 1e-5,
np.float64: 1e-14}),
op_record("digamma", 1, float_dtypes, jtu.rand_positive,
{np.float64: 1e-14}),
op_record("betainc", 3, float_dtypes, jtu.rand_positive,
{np.float64: 1e-14}),
op_record("igamma", 2,
[f for f in float_dtypes if f not in [dtypes.bfloat16, np.float16]],
jtu.rand_positive, {np.float64: 1e-14}),
op_record("igammac", 2,
[f for f in float_dtypes if f not in [dtypes.bfloat16, np.float16]],
jtu.rand_positive, {np.float64: 1e-14}),
op_record("erf", 1, float_dtypes, jtu.rand_small),
op_record("erfc", 1, float_dtypes, jtu.rand_small),
# TODO(b/142976030): the approximation of erfinf used by XLA is only
# accurate to float32 precision.
op_record("erf_inv", 1, float_dtypes, jtu.rand_small,
{np.float64: 1e-9}),
op_record("bessel_i0e", 1, float_dtypes, jtu.rand_default),
op_record("bessel_i1e", 1, float_dtypes, jtu.rand_default),
op_record("real", 1, complex_dtypes, jtu.rand_default),
op_record("imag", 1, complex_dtypes, jtu.rand_default),
op_record("complex", 2, complex_elem_dtypes, jtu.rand_default),
op_record("conj", 1, complex_elem_dtypes + complex_dtypes,
jtu.rand_default),
op_record("abs", 1, default_dtypes + complex_dtypes, jtu.rand_default),
op_record("pow", 2, float_dtypes + complex_dtypes, jtu.rand_positive),
op_record("bitwise_and", 2, bool_dtypes, jtu.rand_small),
op_record("bitwise_not", 1, bool_dtypes, jtu.rand_small),
op_record("bitwise_or", 2, bool_dtypes, jtu.rand_small),
op_record("bitwise_xor", 2, bool_dtypes, jtu.rand_small),
op_record("population_count", 1, int_dtypes + uint_dtypes, jtu.rand_int),
op_record("add", 2, default_dtypes + complex_dtypes, jtu.rand_small),
op_record("sub", 2, default_dtypes + complex_dtypes, jtu.rand_small),
op_record("mul", 2, default_dtypes + complex_dtypes, jtu.rand_small),
op_record("div", 2, default_dtypes + complex_dtypes, jtu.rand_nonzero),
op_record("rem", 2, default_dtypes, jtu.rand_nonzero),
op_record("max", 2, all_dtypes, jtu.rand_small),
op_record("min", 2, all_dtypes, jtu.rand_small),
op_record("eq", 2, all_dtypes, jtu.rand_some_equal),
op_record("ne", 2, all_dtypes, jtu.rand_small),
op_record("ge", 2, default_dtypes, jtu.rand_small),
op_record("gt", 2, default_dtypes, jtu.rand_small),
op_record("le", 2, default_dtypes, jtu.rand_small),
op_record("lt", 2, default_dtypes, jtu.rand_small),
]
class LaxTest(jtu.JaxTestCase):
"""Numerical tests for LAX operations."""
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(
rec.op, shapes, itertools.repeat(dtype)),
"op_name": rec.op, "rng_factory": rec.rng_factory, "shapes": shapes,
"dtype": dtype}
for shape_group in compatible_shapes
for shapes in itertools.combinations_with_replacement(shape_group, rec.nargs)
for dtype in rec.dtypes)
for rec in LAX_OPS))
def testOp(self, op_name, rng_factory, shapes, dtype):
rng = rng_factory(self.rng())
args_maker = lambda: [rng(shape, dtype) for shape in shapes]
op = getattr(lax, op_name)
self._CompileAndCheck(op, args_maker)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(
rec.op, shapes, itertools.repeat(dtype)),
"op_name": rec.op, "rng_factory": rec.rng_factory, "shapes": shapes,
"dtype": dtype, "tol": rec.tol}
for shape_group in compatible_shapes
for shapes in itertools.combinations_with_replacement(shape_group, rec.nargs)
for dtype in rec.dtypes)
for rec in LAX_OPS))
def testOpAgainstNumpy(self, op_name, rng_factory, shapes, dtype, tol):
if (not FLAGS.jax_enable_x64 and op_name == "nextafter"
and dtype == np.float64):
raise SkipTest("64-bit mode disabled")
rng = rng_factory(self.rng())
args_maker = lambda: [rng(shape, dtype) for shape in shapes]
op = getattr(lax, op_name)
numpy_op = getattr(lax_reference, op_name)
self._CheckAgainstNumpy(numpy_op, op, args_maker, tol=tol)
# TODO test shift_left, shift_right_arithmetic, shift_right_logical
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_from_dtype={}_to_dtype={}_weak_type={}".format(
from_dtype, to_dtype, weak_type),
"from_dtype": from_dtype, "to_dtype": to_dtype, "weak_type": weak_type}
for from_dtype, to_dtype in itertools.product(
[None, np.float32, np.int32, "float32", "int32"], repeat=2)
for weak_type in [True, False]))
def testConvertElementType(self, from_dtype, to_dtype, weak_type):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng((2, 3), from_dtype)]
op = lambda x: lax.convert_element_type(x, to_dtype, weak_type)
self._CompileAndCheck(op, args_maker)
x = rng((1,), from_dtype)
out = op(x)
self.assertEqual(out.dtype, dtypes.canonicalize_dtype(to_dtype or x.dtype))
self.assertEqual(out.aval.weak_type, weak_type)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_from_dtype={}_to_dtype={}"
.format(from_dtype, to_dtype),
"from_dtype": from_dtype, "to_dtype": to_dtype}
for from_dtype, to_dtype in itertools.product(
[np.float32, np.int32, "float32", "int32"], repeat=2)))
def testConvertElementTypeAgainstNumpy(self, from_dtype, to_dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng((2, 3), from_dtype)]
op = lambda x: lax.convert_element_type(x, to_dtype)
numpy_op = lambda x: lax_reference.convert_element_type(x, to_dtype)
self._CheckAgainstNumpy(numpy_op, op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_from_dtype={}_to_dtype={}"
.format(from_dtype, to_dtype),
"from_dtype": from_dtype, "to_dtype": to_dtype}
for from_dtype, to_dtype in itertools.product(
[np.float32, np.int32, "float32", "int32"], repeat=2)))
def testBitcastConvertType(self, from_dtype, to_dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng((2, 3), from_dtype)]
op = lambda x: lax.bitcast_convert_type(x, to_dtype)
self._CompileAndCheck(op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_from_dtype={}_to_dtype={}"
.format(from_dtype, to_dtype),
"from_dtype": from_dtype, "to_dtype": to_dtype}
for from_dtype, to_dtype in itertools.product(
[np.float32, np.int32, "float32", "int32"], repeat=2)))
def testBitcastConvertTypeAgainstNumpy(self, from_dtype, to_dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng((2, 3), from_dtype)]
op = lambda x: lax.bitcast_convert_type(x, to_dtype)
numpy_op = lambda x: lax_reference.bitcast_convert_type(x, to_dtype)
self._CheckAgainstNumpy(numpy_op, op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_min_shape={}_operand_shape={}_max_shape={}".format(
jtu.format_shape_dtype_string(min_shape, dtype),
jtu.format_shape_dtype_string(operand_shape, dtype),
jtu.format_shape_dtype_string(max_shape, dtype)),
"min_shape": min_shape, "operand_shape": operand_shape,
"max_shape": max_shape, "dtype": dtype}
for min_shape, operand_shape, max_shape in [
[(), (2, 3), ()],
[(2, 3), (2, 3), ()],
[(), (2, 3), (2, 3)],
[(2, 3), (2, 3), (2, 3)],
]
for dtype in default_dtypes))
def testClamp(self, min_shape, operand_shape, max_shape, dtype):
rng = jtu.rand_default(self.rng())
shapes = [min_shape, operand_shape, max_shape]
args_maker = lambda: [rng(shape, dtype) for shape in shapes]
self._CompileAndCheck(lax.clamp, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_min_shape={}_operand_shape={}_max_shape={}".format(
jtu.format_shape_dtype_string(min_shape, dtype),
jtu.format_shape_dtype_string(operand_shape, dtype),
jtu.format_shape_dtype_string(max_shape, dtype)),
"min_shape": min_shape, "operand_shape": operand_shape,
"max_shape": max_shape, "dtype": dtype}
for min_shape, operand_shape, max_shape in [
[(), (2, 3), ()],
[(2, 3), (2, 3), ()],
[(), (2, 3), (2, 3)],
[(2, 3), (2, 3), (2, 3)],
]
for dtype in default_dtypes))
def testClampAgainstNumpy(self, min_shape, operand_shape, max_shape, dtype):
rng = jtu.rand_default(self.rng())
shapes = [min_shape, operand_shape, max_shape]
args_maker = lambda: [rng(shape, dtype) for shape in shapes]
self._CheckAgainstNumpy(lax_reference.clamp, lax.clamp, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_dim={}_baseshape=[{}]_dtype={}_narrs={}".format(
dim, ",".join(str(d) for d in base_shape), np.dtype(dtype).name,
num_arrs),
"dim": dim, "base_shape": base_shape, "dtype": dtype, "num_arrs": num_arrs}
for num_arrs in [3]
for dtype in default_dtypes
for base_shape in [(4,), (3, 4), (2, 3, 4)]
for dim in range(len(base_shape))))
def testConcatenate(self, dim, base_shape, dtype, num_arrs):
rng = jtu.rand_default(self.rng())
shapes = [base_shape[:dim] + (size,) + base_shape[dim+1:]
for size, _ in zip(itertools.cycle([3, 1, 4]), range(num_arrs))]
args_maker = lambda: [rng(shape, dtype) for shape in shapes]
op = lambda *args: lax.concatenate(args, dim)
self._CompileAndCheck(op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_dim={}_baseshape=[{}]_dtype={}_narrs={}".format(
dim, ",".join(str(d) for d in base_shape), np.dtype(dtype).name,
num_arrs),
"dim": dim, "base_shape": base_shape, "dtype": dtype, "num_arrs": num_arrs}
for num_arrs in [3]
for dtype in default_dtypes
for base_shape in [(4,), (3, 4), (2, 3, 4)]
for dim in range(len(base_shape))))
def testConcatenateAgainstNumpy(self, dim, base_shape, dtype, num_arrs):
rng = jtu.rand_default(self.rng())
shapes = [base_shape[:dim] + (size,) + base_shape[dim+1:]
for size, _ in zip(itertools.cycle([3, 1, 4]), range(num_arrs))]
args_maker = lambda: [rng(shape, dtype) for shape in shapes]
op = lambda *args: lax.concatenate(args, dim)
numpy_op = lambda *args: lax_reference.concatenate(args, dim)
self._CheckAgainstNumpy(numpy_op, op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_strides={}_padding={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding}
for lhs_shape, rhs_shape in [
((b, i, 9, 10), (j, i, 4, 5))
for b, i, j in itertools.product([2, 3], repeat=3)]
for dtype in float_dtypes
for strides in [(1, 1), (1, 2), (2, 1)]
for padding in ["VALID", "SAME"]))
def testConv(self, lhs_shape, rhs_shape, dtype, strides, padding):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.conv(lhs, rhs, strides, padding)
self._CompileAndCheck(fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_strides={}_padding={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding}
for lhs_shape, rhs_shape in [
((b, i, 9, 10), (j, i, 4, 5))
for b, i, j in itertools.product([2, 3], repeat=3)]
for dtype in float_dtypes
for strides in [(1, 1), (1, 2), (2, 1)]
for padding in ["VALID", "SAME"]))
def testConvAgainstNumpy(self, lhs_shape, rhs_shape, dtype, strides, padding):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
op = lambda lhs, rhs: lax.conv(lhs, rhs, strides, padding)
numpy_op = lambda lhs, rhs: lax_reference.conv(lhs, rhs, strides, padding)
self._CheckAgainstNumpy(numpy_op, op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}"
"_lhs_dilation={}_rhs_dilation={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
strides, padding, lhs_dilation, rhs_dilation),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "lhs_dilation": lhs_dilation,
"rhs_dilation": rhs_dilation}
for lhs_shape, rhs_shape in [
((b, i, 9, 10), (j, i, 4, 5))
for b, i, j in itertools.product([1, 2, 3], repeat=3)]
for dtype in float_dtypes
for strides in [(1, 1), (1, 2), (2, 1)]
for padding in [((0, 0), (0, 0)), ((1, 2), (2, 0))]
for lhs_dilation, rhs_dilation in itertools.product(
[(1, 1), (1, 2), (2, 2)], repeat=2)))
def testConvWithGeneralPadding(self, lhs_shape, rhs_shape, dtype, strides,
padding, lhs_dilation, rhs_dilation):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.conv_with_general_padding(
lhs, rhs, strides, padding, lhs_dilation, rhs_dilation)
self._CompileAndCheck(fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}"
"_lhs_dilation={}_rhs_dilation={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
strides, padding, lhs_dilation, rhs_dilation),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "lhs_dilation": lhs_dilation,
"rhs_dilation": rhs_dilation}
for lhs_shape, rhs_shape in [
((b, i, 9, 10), (j, i, 4, 5))
for b, i, j in itertools.product([1, 2, 3], repeat=3)]
for dtype in [np.float32] for strides in [(1, 1), (1, 2), (2, 1)]
for padding in [((0, 0), (0, 0)), ((1, 2), (2, 0))]
for lhs_dilation, rhs_dilation in itertools.product(
[(1, 1), (1, 2), (2, 2)], repeat=2)))
def testConvWithGeneralPaddingAgainstNumpy(
self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation,
rhs_dilation):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.conv_with_general_padding(
lhs, rhs, strides, padding, lhs_dilation, rhs_dilation,
precision=lax.Precision.HIGHEST)
def numpy_fun(lhs, rhs):
return lax_reference.conv_with_general_padding(
lhs, rhs, strides, padding, lhs_dilation, rhs_dilation)
self._CheckAgainstNumpy(numpy_fun, fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}"
"_lhs_dilation={}_rhs_dilation={}"
"_dims={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
strides, padding, lhs_dilation, rhs_dilation,
",".join(dim_nums)),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "lhs_dilation": lhs_dilation,
"rhs_dilation": rhs_dilation, "dimension_numbers": dim_nums,
"feature_group_count": feature_group_count,
"batch_group_count": batch_group_count, "perms": perms}
for batch_group_count, feature_group_count in [(1, 1), (2, 1), (1, 2)]
for lhs_shape, rhs_shape in [
((b * batch_group_count, i * feature_group_count, 9, w),
(j * feature_group_count * batch_group_count, i, 4, 5))
for w in [0, 10]
for b, i, j in itertools.product([2, 3], repeat=3)]
for dtype in inexact_dtypes for strides in [(1, 1), (2, 1)]
for padding in [((1, 2), (2, 0)), ((10, 8), (7, 13))]
for lhs_dilation, rhs_dilation in itertools.product(
[(1, 1), (1, 2), (1, 4)], repeat=2)
for dim_nums, perms in [
(("NCHW", "OIHW", "NCHW"), ([0, 1, 2, 3], [0, 1, 2, 3])),
(("NHWC", "HWIO", "NHWC"), ([0, 2, 3, 1], [2, 3, 1, 0])),
(("NCHW", "HWIO", "NHWC"), ([0, 1, 2, 3], [2, 3, 1, 0])),
]))
def testConvGeneralDilated(self, lhs_shape, rhs_shape, dtype, strides,
padding, lhs_dilation, rhs_dilation,
feature_group_count, batch_group_count,
dimension_numbers, perms):
rng = jtu.rand_small(self.rng())
lhs_perm, rhs_perm = perms # permute to compatible shapes
def args_maker():
return [lax.transpose(rng(lhs_shape, dtype), lhs_perm),
lax.transpose(rng(rhs_shape, dtype), rhs_perm)]
def fun(lhs, rhs):
return lax.conv_general_dilated(
lhs, rhs, strides, padding, lhs_dilation, rhs_dilation,
dimension_numbers, feature_group_count=feature_group_count,
batch_group_count=batch_group_count)
self._CompileAndCheck(fun, args_maker)
def testConvGeneralDilatedPatchesOverlapping1D(self):
lhs = np.array([[1]], np.float32).reshape((1, 1))
patches = lax.conv_general_dilated_patches(
lhs=lhs,
filter_shape=(),
window_strides=(),
padding='SAME'
)
self.assertAllClose(lhs, patches)
dn = ('NHC', 'OIH', 'NHC')
lhs = np.array([1, 2, 3, 4, 5], np.float32).reshape((1, -1, 1))
patches = lax.conv_general_dilated_patches(
lhs=lhs,
filter_shape=(2,),
window_strides=(2,),
padding='VALID',
dimension_numbers=dn
)
self.assertAllClose(
np.array([[1, 2],
[3, 4]], np.float32).reshape((1, 2, 2)), patches)
patches = lax.conv_general_dilated_patches(
lhs=lhs,
filter_shape=(3,),
window_strides=(1,),
padding='SAME',
dimension_numbers=dn
)
self.assertAllClose(
np.array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4],
[3, 4, 5],
[4, 5, 0]], np.float32).reshape((1, 5, 3)), patches)
patches = lax.conv_general_dilated_patches(
lhs=lhs,
filter_shape=(3,),
window_strides=(1,),
padding='SAME',
rhs_dilation=(2,),
dimension_numbers=dn
)
self.assertAllClose(
np.array([[0, 1, 3],
[0, 2, 4],
[1, 3, 5],
[2, 4, 0],
[3, 5, 0]], np.float32).reshape((1, 5, 3)), patches)
def testConvGeneralDilatedPatchesOverlapping2D(self):
lhs = np.array([[1, 2, 3],
[4, 5, 6]], np.float32).reshape((1, 2, 3, 1))
patches = lax.conv_general_dilated_patches(
lhs=lhs,
filter_shape=(2, 2),
window_strides=(1, 1),
padding='SAME',
dimension_numbers=('NHWC', 'OIHW', 'NHWC')
)
self.assertAllClose(np.array([[1, 2, 4, 5],
[2, 3, 5, 6],
[3, 0, 6, 0],
[4, 5, 0, 0],
[5, 6, 0, 0],
[6, 0, 0, 0]],
np.float32).reshape((1, 2, 3, 4)), patches)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_filter_shape={}_strides={}_padding={}"
"_dims={}_precision={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(filter_shape, dtype),
strides,
padding,
"None" if dim_nums is None else ",".join(dim_nums),
precision
),
"lhs_shape": lhs_shape,
"filter_shape": filter_shape,
"dtype": dtype,
"strides": strides,
"padding": padding,
"dimension_numbers": dim_nums,
"precision": precision
}
for dtype in inexact_dtypes
for lhs_shape, filter_shape, strides, padding, dim_nums in [
((2, 5), (), (), [], ("NC", "OI", "CN")),
((2, 3, 4), (2,), (2,), [(0, 2)], ("CNH", "OHI", "HNC")),
((3, 1, 4, 5), (1, 3), (1, 3), [(3, 1), (2, 2)],
("NCHW", "OIHW", "NCHW")),
((3, 2, 5, 6), (4, 3), (4, 3), [(5, 2), (2, 4)],
None),
((1, 2, 3, 4), (1, 1), (1, 1), [(0, 0), (0, 0)],
("NCWH", "OHWI", "CNHW")),
((1, 2, 3, 4), (3, 2), (1, 1), [(0, 0), (0, 0)],
("CWHN", "HOWI", "NCHW")),
((2, 3, 4, 5, 6), (2, 1, 3), (2, 1, 3), [(1, 2), (5, 3), (3, 5)],
("NHWDC", "HDIWO", "DCWNH"))
]
for precision in [None,
lax.Precision.DEFAULT,
lax.Precision.HIGH,
lax.Precision.HIGHEST]
))
def testConvGeneralDilatedPatchesNonOverlapping(self,
lhs_shape,
filter_shape,
dtype,
strides,
padding,
dimension_numbers,
precision):
rng = jtu.rand_small(self.rng())
lhs = rng(lhs_shape, dtype)
if dimension_numbers is None:
lhs_spec, rhs_spec, out_spec = "NCHW", "OIHW", "NCHW"
else:
lhs_spec, rhs_spec, out_spec = dimension_numbers
filter_spec = ''.join(c for c in rhs_spec if c not in ('I', 'O'))
patches_spec = out_spec.replace('C', 'C' + filter_spec.lower())
full_padding = []
for c in lhs_spec:
if c in ('N', 'C'):
full_padding += [(0, 0)]
else:
full_padding += [padding[filter_spec.index(c)]]
lhs_padded = np.pad(lhs, full_padding, 'constant')
out = lax.transpose(lhs_padded, [lhs_spec.index(c) for c in out_spec])
patches = lax.conv_general_dilated_patches(
lhs=lhs,
filter_shape=filter_shape,
window_strides=strides,
padding=padding,
dimension_numbers=dimension_numbers,
precision=precision
)
source = []
# Test that output spatial shape is factored into `#patches x patch_size`.
for c in out_spec:
out_c = out.shape[out_spec.index(c)]
patch_c = patches.shape[out_spec.index(c)]
if c == 'N':
self.assertEqual(out_c, patch_c)
elif c == 'C':
self.assertEqual(out_c * np.prod(filter_shape), patch_c)
else:
self.assertEqual(out_c, patch_c * filter_shape[filter_spec.index(c)])
source += [patches_spec.index(c), patches_spec.index(c.lower())]
# Test that stacking patches together gives the source image, padded.
c = out_spec.index('C')
patches = patches.reshape(patches.shape[:c] +
(lhs_shape[lhs_spec.index('C')],) +
filter_shape +
patches.shape[c + 1:]
)
patches = np.moveaxis(patches, source, range(len(source)))
for i in range(len(filter_shape)):
patches = patches.reshape(patches.shape[:i] + (-1,) +
patches.shape[2 + i:])
patches = np.moveaxis(
patches,
range(len(filter_shape)),
[out_spec.index(c) for c in out_spec if c not in ('N', 'C')])
self.assertAllClose(out, patches)
# TODO(mattjj): test conv_general_dilated against numpy
def testConv0DIsDot(self):
rng = jtu.rand_default(self.rng())
def args_maker():
return [rng((10, 5), np.float32), rng((5, 7), np.float32)]
jnp_fun = partial(lax.conv_general_dilated, window_strides=(),
padding='VALID', dimension_numbers=('NC', 'IO', 'NC'))
self._CompileAndCheck(jnp_fun, args_maker)
self._CheckAgainstNumpy(np.dot, jnp_fun, args_maker, tol=.1)
@staticmethod
def _conv_transpose_via_grad(data, kernel, strides, padding,
rhs_dilation=None, dimension_numbers=None):
"""Helper method: calculates conv transpose via grad for testing."""
assert len(data.shape) == len(kernel.shape)
nspatial = len(data.shape) - 2
one = (1,) * nspatial
rhs_dilation = rhs_dilation or one
dn = lax.conv_dimension_numbers(data.shape, kernel.shape,
dimension_numbers)
in_shape = np.take(data.shape, dn.lhs_spec)
in_sdims = in_shape[2:]
k_shape = np.take(kernel.shape, dn.rhs_spec)
k_sdims = k_shape[2:]
e_k_sdims = [(k-1) * r + 1 for k, r in zip(k_sdims, rhs_dilation)]
if padding == 'VALID':
o_sdims = [in_sdims[i]*strides[i] + max(e_k_sdims[i]-strides[i],0)
for i in range(nspatial)]
elif padding == 'SAME':
o_sdims = [in_sdims[i]*strides[i] for i in range(nspatial)]
o_shape = [in_shape[0], k_shape[1]] + o_sdims
out_spec_inv = [x[0] for x in
sorted(enumerate(dn.out_spec), key=lambda x: x[1])]
o_layout = np.take(np.array(o_shape), out_spec_inv)
placeholder = np.ones(o_layout, data.dtype)
conv = lambda x: lax.conv_general_dilated(x, kernel, strides, padding,
one, rhs_dilation, dn)
_, g = api.vjp(conv, placeholder)
return g(data)[0]
@staticmethod
def _transpose_conv_kernel(data, kernel, dimension_numbers):
dn = lax.conv_dimension_numbers(data.shape, kernel.shape,
dimension_numbers)
spatial_axes = np.array(dn.rhs_spec)[2:]
for axis in spatial_axes:
kernel = np.flip(kernel, axis)
kernel = np.swapaxes(kernel, dn.rhs_spec[0], dn.rhs_spec[1])
return kernel
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "rhs_dilation": rhs_dilation,
"dspec": dspec}
for lhs_shape, rhs_shape in [
((b, 9, 10, i), (k, k, j, i)) # NB: i,j flipped in RHS for transpose
for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])]
for dtype in float_dtypes
for strides in [(1, 1), (1, 2), (2, 1), (2, 2), (3, 3)]
for padding in ["VALID", "SAME"]
for dspec in [('NHWC', 'HWIO', 'NHWC'),]
for rhs_dilation in [None, (2, 2)]))
@jtu.skip_on_flag("jax_skip_slow_tests", True)
def testConvTranspose2DT(self, lhs_shape, rhs_shape, dtype, strides,
padding, dspec, rhs_dilation):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
# NB: this test calculates conv_transpose performing identically to the
# lhs-grad of conv.
def fun(lhs, rhs):
return lax.conv_transpose(lhs, rhs, strides, padding,
rhs_dilation=rhs_dilation,
dimension_numbers=dspec,
transpose_kernel=True)
def fun_via_grad(lhs, rhs):
return self._conv_transpose_via_grad(lhs, rhs, strides, padding,
rhs_dilation=rhs_dilation,
dimension_numbers=dspec)
# NB: below just checks for agreement, we're not calling numpy.
self._CheckAgainstNumpy(fun_via_grad, fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "rhs_dilation": rhs_dilation,
"dspec": dspec}
for lhs_shape, rhs_shape in [
((b, 9, 10, i), (k, k, i, j))
for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])]
for dtype in float_dtypes
for strides in [(1, 1), (1, 2), (2, 1), (2, 2), (3, 3)]
for padding in ["VALID", "SAME"]
for dspec in [('NHWC', 'HWIO', 'NHWC'),]
for rhs_dilation in [None, (2, 2)]))
@jtu.skip_on_flag("jax_skip_slow_tests", True)
def testConvTranspose2D(self, lhs_shape, rhs_shape, dtype, strides,
padding, dspec, rhs_dilation):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.conv_transpose(lhs, rhs, strides, padding,
rhs_dilation=rhs_dilation,
dimension_numbers=dspec,
transpose_kernel=False)
def fun_via_grad(lhs, rhs):
rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec)
return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding,
rhs_dilation=rhs_dilation,
dimension_numbers=dspec)
# NB: below just checks for agreement, we're not calling numpy.
self._CheckAgainstNumpy(fun_via_grad, fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "rhs_dilation": rhs_dilation,
"dspec": dspec}
for lhs_shape, rhs_shape in [
((b, 10, i), (k, i, j))
for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])]
for dtype in float_dtypes
for strides in [(1,), (2,), (3,)]
for padding in ["VALID", "SAME"]
for dspec in [('NHC', 'HIO', 'NHC'),]
for rhs_dilation in [None, (2,)]))
def testConvTranspose1D(self, lhs_shape, rhs_shape, dtype, strides,
padding, dspec, rhs_dilation):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.conv_transpose(lhs, rhs, strides, padding,
dimension_numbers=dspec,
rhs_dilation=rhs_dilation,
transpose_kernel=False)
def fun_via_grad(lhs, rhs):
rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec)
return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding,
rhs_dilation=rhs_dilation,
dimension_numbers=dspec)
# NB: below just checks for agreement, we're not calling numpy.
self._CheckAgainstNumpy(fun_via_grad, fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"strides": strides, "padding": padding, "rhs_dilation": rhs_dilation,
"dspec": dspec}
for lhs_shape, rhs_shape in [
((b, i), (i, j))
for b, i, j in itertools.product([2,3],[2,3],[2,3])]
for dtype in float_dtypes
for strides in [()]
for padding in ["VALID", "SAME"]
for dspec in [('NC', 'IO', 'NC'),]
for rhs_dilation in [None, ()]))
def testConvTranspose0D(self, lhs_shape, rhs_shape, dtype, strides,
padding, dspec, rhs_dilation):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.conv_transpose(lhs, rhs, strides, padding,
dimension_numbers=dspec,
rhs_dilation=rhs_dilation,
transpose_kernel=False)
def fun_via_grad(lhs, rhs):
rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec)
return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding,
rhs_dilation=rhs_dilation,
dimension_numbers=dspec)
# NB: below just checks for agreement, we're not calling numpy.
self._CheckAgainstNumpy(fun_via_grad, fun, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_lhs_shape={}_rhs_shape={}_precision={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
precision),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"precision": precision}
for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)]
for dtype in all_dtypes
for precision in [None, lax.Precision.DEFAULT, lax.Precision.HIGH,
lax.Precision.HIGHEST,
(lax.Precision.DEFAULT, lax.Precision.HIGHEST)]))
def testDot(self, lhs_shape, rhs_shape, dtype, precision):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
self._CompileAndCheck(partial(lax.dot, precision=precision), args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_lhs_shape={}_rhs_shape={}".format(
jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype)),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype}
for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)]
for dtype in all_dtypes))
def testDotAgainstNumpy(self, lhs_shape, rhs_shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
tol = {
np.float16: 1e-2,
np.float64: max(jtu.default_tolerance()[np.dtype(np.float64)], 1e-14),
np.complex128: max(jtu.default_tolerance()[np.dtype(np.complex128)],
1e-14)
}
lax_op = partial(lax.dot, precision=lax.Precision.HIGHEST)
self._CheckAgainstNumpy(lax_reference.dot, lax_op, args_maker, tol=tol)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_lhs_contracting={}_rhs_contracting={}"
.format(jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
lhs_contracting, rhs_contracting),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"lhs_contracting": lhs_contracting, "rhs_contracting": rhs_contracting}
for lhs_shape, rhs_shape, lhs_contracting, rhs_contracting in [
[(5,), (5,), [0], [0]],
[(5, 7), (5,), [0], [0]],
[(7, 5), (5,), [1], [0]],
[(3, 5), (2, 5), [1], [1]],
[(5, 3), (5, 2), [0], [0]],
[(5, 3, 2), (5, 2, 4), [0], [0]],
[(5, 3, 2), (5, 2, 4), [0,2], [0,1]],
[(5, 3, 2), (3, 5, 2, 4), [0,2], [1,2]],
[(1, 2, 2, 3), (1, 2, 3, 1), [1], [1]],
[(3, 2), (2, 4), [1], [0]],
]
for dtype in all_dtypes))
def testDotGeneralContractOnly(self, lhs_shape, rhs_shape, dtype,
lhs_contracting, rhs_contracting):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
dimension_numbers = ((lhs_contracting, rhs_contracting), ([], []))
def fun(lhs, rhs):
return lax.dot_general(lhs, rhs, dimension_numbers)
self._CompileAndCheck(fun, args_maker, check_dtypes=False)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_dimension_numbers={}"
.format(jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
dimension_numbers),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"dimension_numbers": dimension_numbers}
for lhs_shape, rhs_shape, dimension_numbers in [
((3, 3, 2), (3, 2, 4), (([2], [1]), ([0], [0]))),
((3, 3, 2), (2, 3, 4), (([2], [0]), ([0], [1]))),
((3, 4, 2, 4), (3, 4, 3, 2), (([2], [3]), ([0, 1], [0, 1]))),
]
for dtype in all_dtypes))
def testDotGeneralContractAndBatch(self, lhs_shape, rhs_shape, dtype,
dimension_numbers):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
def fun(lhs, rhs):
return lax.dot_general(lhs, rhs, dimension_numbers)
self._CompileAndCheck(fun, args_maker, check_dtypes=False)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name":
"_lhs_shape={}_rhs_shape={}_dimension_numbers={}"
.format(jtu.format_shape_dtype_string(lhs_shape, dtype),
jtu.format_shape_dtype_string(rhs_shape, dtype),
dimension_numbers),
"lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype,
"dimension_numbers": dimension_numbers}
for lhs_shape, rhs_shape, dimension_numbers in [
((3, 3, 2), (3, 2, 4), (([2], [1]), ([0], [0]))),
((3, 3, 2), (2, 3, 4), (([2], [0]), ([0], [1]))),
((3, 4, 2, 4), (3, 4, 3, 2), (([2], [3]), ([0, 1], [0, 1]))),
]
for dtype in all_dtypes))
def testDotGeneralAgainstNumpy(self, lhs_shape, rhs_shape, dtype,
dimension_numbers):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
op = lambda x, y: lax.dot_general(x, y, dimension_numbers)
numpy_op = lambda x, y: lax_reference.dot_general(x, y, dimension_numbers)
self._CheckAgainstNumpy(numpy_op, op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_shape={}_dtype={}_broadcast_sizes={}".format(
shape, np.dtype(dtype).name, broadcast_sizes),
"shape": shape, "dtype": dtype, "broadcast_sizes": broadcast_sizes}
for shape in [(), (2, 3)]
for dtype in default_dtypes
for broadcast_sizes in [(), (2,), (1, 2)]))
def testBroadcast(self, shape, dtype, broadcast_sizes):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
op = lambda x: lax.broadcast(x, broadcast_sizes)
self._CompileAndCheck(op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": "_shape={}_broadcast_sizes={}".format(
jtu.format_shape_dtype_string(shape, dtype), broadcast_sizes),
"shape": shape, "dtype": dtype, "broadcast_sizes": broadcast_sizes}
for shape in [(), (2, 3)]